IDEAS home Printed from https://ideas.repec.org/p/yor/hectdg/09-18.html
   My bibliography  Save this paper

Quantile Regression Analysis of the Rational Addiction Model: Making unobservable heterogeneity observable

Author

Listed:
  • Laporte A
  • Karimova A
  • Ferguson B

Abstract

The Rational Addiction (RA) model assumes that individual decisions about the consumption of harmful and addictive commodities are made on a rational basis (Becker and Murphy, 1988). In this context, rational means forward looking, i.e. a tendency to take account of future consequences of current consumption decisions. Different individuals may well attach different weights to the present relative to the future. The degree to which an individual is forward looking in her consumption decisions is revealed not by her current consumption level but rather by the time path of her consumption of an addictive commodity. Hence, the need to estimate a forward looking second order difference equation (SODE) as part of the process of testing the RA model. Most studies using micro level data estimate a single SODE for the whole sample. This involves estimating an average propensity to be forward looking for the entire sample, even when it is believed that different fully rational individuals in the same sample may have different propensities to be forward looking. Forward looking behaviour is an aspect of treating the consumption of an addictive commodity as part of an inter-temporal optimization problem. Inter-temporal optimization is characterized by what are known as saddle point dynamics and the information about an individual’s propensity to be forward looking is contained in what are known as the characteristic roots of the equation (Ferguson, 2003). In a sample of heterogeneous individuals we expect propensity to be forward looking to differ across individuals and the best way to identify these differences is by looking at the dynamic behaviour of the individual consumption paths. Estimating a common SODE for everyone hides this key difference. In this paper, we make the argument that the best place to look for differences in individual propensities to be forward looking is in dynamic behaviour considered at different points in the distribution of the consumption of an addictive commodity. To do this we adopt techniques of Quantile Regression, (QR) estimating RA type difference equations in consumption across quantiles of cigarette consumption. We use panel data to ensure that we are examining the behaviour of individuals across time. Our hypothesis is that we will find differences in the degree of forward looking behaviour characterizing the time paths of consumption across quantiles in the micro-level data.

Suggested Citation

  • Laporte A & Karimova A & Ferguson B, 2009. "Quantile Regression Analysis of the Rational Addiction Model: Making unobservable heterogeneity observable," Health, Econometrics and Data Group (HEDG) Working Papers 09/18, HEDG, c/o Department of Economics, University of York.
    • Handle: RePEc:yor:hectdg:09/18
    as

    Download full text from publisher

    File URL: https://www.york.ac.uk/media/economics/documents/herc/wp/09_18.pdf
    File Function: Main text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Badi H. Baltagi & Ingo Geishecker, 2006. "Rational alcohol addiction: evidence from the Russian longitudinal monitoring survey," Health Economics, John Wiley & Sons, Ltd., vol. 15(9), pages 893-914, September.
    2. Andrew M. Jones & José M. Labeaga, 2003. "Individual heterogeneity and censoring in panel data estimates of tobacco expenditure," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(2), pages 157-177.
    3. Becker, Gary S & Grossman, Michael & Murphy, Kevin M, 1994. "An Empirical Analysis of Cigarette Addiction," American Economic Review, American Economic Association, vol. 84(3), pages 396-418, June.
    4. Koenker, Roger & Xiao, Zhijie, 2006. "Quantile Autoregression," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 980-990, September.
    5. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Piccoli, Luca & Tiezzi, Silvia, 2021. "Rational addiction and time-consistency: An empirical test," Journal of Health Economics, Elsevier, vol. 80(C).
    2. Andrew M. Jones & Audrey Laporte & Nigel Rice & Eugenio Zucchelli, 2019. "Dynamic panel data estimation of an integrated Grossman and Becker–Murphy model of health and addiction," Empirical Economics, Springer, vol. 56(2), pages 703-733, February.
    3. Laporte, Audrey & Dass, Adrian Rohit & Ferguson, Brian S., 2017. "Is the Rational Addiction model inherently impossible to estimate?," Journal of Health Economics, Elsevier, vol. 54(C), pages 161-175.
    4. Pierani, P.; Tiezzi, S.;, 2017. "Rational addiction and time consistency:an empirical test," Health, Econometrics and Data Group (HEDG) Working Papers 17/05, HEDG, c/o Department of Economics, University of York.
    5. David Aristei & Luca Pieroni, 2010. "Habits, Complementarities and Heterogeneity in Alcohol and Tobacco Demand: A Multivariate Dynamic Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 428-457, August.
    6. Badi H. Baltagi & Ingo Geishecker, 2006. "Rational alcohol addiction: evidence from the Russian longitudinal monitoring survey," Health Economics, John Wiley & Sons, Ltd., vol. 15(9), pages 893-914, September.
    7. Gabriel A. Picone & Frank Sloan & Justin G. Trogdon, 2004. "The effect of the tobacco settlement and smoking bans on alcohol consumption," Health Economics, John Wiley & Sons, Ltd., vol. 13(10), pages 1063-1080, October.
    8. Anne Bretteville-Jensen, 2006. "Drug Demand – Initiation, Continuation and Quitting," De Economist, Springer, vol. 154(4), pages 491-516, December.
    9. Yana Roshchina, 2013. "To drink or not to drink: the microeconomic analysis of alcohol consumption in Russia in 2006-2010," HSE Working papers WP BRP 20/SOC/2013, National Research University Higher School of Economics.
    10. D. Dragone & D. Raggi, 2018. "Testing Rational Addiction: When Lifetime is Uncertain, One Lag is Enough," Working Papers wp1119, Dipartimento Scienze Economiche, Universita' di Bologna.
    11. Luca Bossi & Pedro Gomis-Porqueras & David L. Kelly, 2007. "Optimal Second Best Taxation of Addictive Goods," Working Papers 0708, University of Miami, Department of Economics.
    12. Milda Norkuté & Vasilis Sarafidis & Takashi Yamagata, 2018. "Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors and a Multifactor Error Structure," ISER Discussion Paper 1019, Institute of Social and Economic Research, Osaka University.
    13. Sarafidis, Vasilis & Yamagata, Takashi, 2010. "Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors under Cross-sectional Dependence," MPRA Paper 25182, University Library of Munich, Germany.
    14. Carranza, Luis & Galdon-Sanchez, Jose E. & Gomez-Biscarri, Javier, 2011. "The relationship between investment and large exchange rate depreciations in dollarized economies," Journal of International Money and Finance, Elsevier, vol. 30(7), pages 1265-1279.
    15. Michael Grossman, 2022. "The demand for health turns 50: Reflections," Health Economics, John Wiley & Sons, Ltd., vol. 31(9), pages 1807-1822, September.
    16. Michelle S. Goeree & John C. Ham & Daniela Iorio, 2009. "Caught in the bulimic trap? Persistence and state dependence of bulimia among young women," IEW - Working Papers 447, Institute for Empirical Research in Economics - University of Zurich, revised Jul 2012.
    17. Jones, A. M. & Laporte, A. & Rice, N. & Zucchelli, E., 2014. "A synthesis of the Grossman and Becker-Murphy models of health and addiction: theoretical and empirical implications," Health, Econometrics and Data Group (HEDG) Working Papers 14/07, HEDG, c/o Department of Economics, University of York.
    18. Dragone, Davide & Raggi, Davide, 2021. "Resolving the milk addiction paradox," Journal of Health Economics, Elsevier, vol. 77(C).
    19. David Aristei & Luca Pieroni, 2008. "A double-hurdle approach to modelling tobacco consumption in Italy," Applied Economics, Taylor & Francis Journals, vol. 40(19), pages 2463-2476.
    20. Badi H. Baltagi, 2021. "Dynamic Panel Data Models," Springer Texts in Business and Economics, in: Econometric Analysis of Panel Data, edition 6, chapter 0, pages 187-228, Springer.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:yor:hectdg:09/18. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Jane Rawlings (email available below). General contact details of provider: https://edirc.repec.org/data/deyoruk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.